Overview

Dataset statistics

Number of variables18
Number of observations138845
Missing cells109449
Missing cells (%)4.4%
Duplicate rows6562
Duplicate rows (%)4.7%
Total size in memory19.1 MiB
Average record size in memory144.0 B

Variable types

Numeric14
Text1
Categorical2
DateTime1

Alerts

Dataset has 6562 (4.7%) duplicate rowsDuplicates
dtdlevel is highly overall correlated with sigmaHigh correlation
sigma is highly overall correlated with dtdlevel and 1 other fieldsHigh correlation
sizelevel is highly overall correlated with sigmaHigh correlation
Y is highly imbalanced (85.7%)Imbalance
StkIndx has 1991 (1.4%) missing valuesMissing
dtdlevel has 14551 (10.5%) missing valuesMissing
dtdtrend has 14551 (10.5%) missing valuesMissing
liqnonfinlevel has 16952 (12.2%) missing valuesMissing
liqnonfintrend has 16952 (12.2%) missing valuesMissing
ni2talevel has 8953 (6.4%) missing valuesMissing
ni2tatrend has 8953 (6.4%) missing valuesMissing
sizelevel has 1949 (1.4%) missing valuesMissing
sizetrend has 1949 (1.4%) missing valuesMissing
m2b has 9145 (6.6%) missing valuesMissing
sigma has 6969 (5.0%) missing valuesMissing
DTDmedianNonFin has 6500 (4.7%) missing valuesMissing
liqnonfintrend has 4151 (3.0%) zerosZeros
ni2tatrend has 4405 (3.2%) zerosZeros
sizetrend has 1451 (1.0%) zerosZeros

Reproduction

Analysis started2024-03-01 05:05:13.047512
Analysis finished2024-03-01 05:05:24.725937
Duration11.68 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

CompNo
Real number (ℝ)

Distinct974
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52134.021
Minimum27014
Maximum213058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-01T13:05:24.763791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum27014
5-th percentile27396
Q129416
median33327
Q345104
95-th percentile164770
Maximum213058
Range186044
Interquartile range (IQR)15688

Descriptive statistics

Standard deviation44416.305
Coefficient of variation (CV)0.85196392
Kurtosis3.9092356
Mean52134.021
Median Absolute Deviation (MAD)5387
Skewness2.2546017
Sum7.2385481 × 109
Variance1.9728081 × 109
MonotonicityIncreasing
2024-03-01T13:05:24.822215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30321 1488
 
1.1%
33327 1448
 
1.0%
32554 1188
 
0.9%
27940 780
 
0.6%
27771 768
 
0.6%
31079 752
 
0.5%
34160 736
 
0.5%
34269 730
 
0.5%
46648 612
 
0.4%
32700 600
 
0.4%
Other values (964) 129743
93.4%
ValueCountFrequency (%)
27014 154
 
0.1%
27026 215
0.2%
27044 432
0.3%
27049 132
 
0.1%
27057 379
0.3%
27064 398
0.3%
27081 147
 
0.1%
27085 432
0.3%
27095 354
0.3%
27125 111
 
0.1%
ValueCountFrequency (%)
213058 1
 
< 0.1%
212750 3
 
< 0.1%
212613 3
 
< 0.1%
212539 4
< 0.1%
212535 4
< 0.1%
212411 5
< 0.1%
212072 7
< 0.1%
211853 8
< 0.1%
211801 8
< 0.1%
211730 9
< 0.1%

StkIndx
Real number (ℝ)

MISSING 

Distinct420
Distinct (%)0.3%
Missing1991
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean0.094778594
Minimum-0.46605694
Maximum0.53714506
Zeros0
Zeros (%)0.0%
Negative29444
Negative (%)21.2%
Memory size1.1 MiB
2024-03-01T13:05:24.876745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.46605694
5-th percentile-0.22016641
Q10.025735611
median0.11191686
Q30.1898926
95-th percentile0.32690037
Maximum0.53714506
Range1.003202
Interquartile range (IQR)0.16415699

Descriptive statistics

Standard deviation0.15806432
Coefficient of variation (CV)1.6677217
Kurtosis1.1452967
Mean0.094778594
Median Absolute Deviation (MAD)0.083104719
Skewness-0.66204689
Sum12970.83
Variance0.024984329
MonotonicityNot monotonic
2024-03-01T13:05:24.926625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.015483874 419
 
0.3%
-0.026497118 415
 
0.3%
0.1488779077 415
 
0.3%
0.172900003 415
 
0.3%
0.222931686 415
 
0.3%
0.1453020353 414
 
0.3%
0.2191530226 412
 
0.3%
0.1139063278 412
 
0.3%
0.1436556584 412
 
0.3%
0.052483688 411
 
0.3%
Other values (410) 132714
95.6%
(Missing) 1991
 
1.4%
ValueCountFrequency (%)
-0.466056938 381
0.3%
-0.396786875 377
0.3%
-0.389790726 382
0.3%
-0.388840878 384
0.3%
-0.383806412 383
0.3%
-0.373725993 389
0.3%
-0.370080615 377
0.3%
-0.342654442 374
0.3%
-0.28619143 308
0.2%
-0.28178125 376
0.3%
ValueCountFrequency (%)
0.5371450583 265
0.2%
0.4911946246 378
0.3%
0.4671174103 374
0.3%
0.4656900247 372
0.3%
0.4551854396 379
0.3%
0.4356293691 267
0.2%
0.3913071653 276
0.2%
0.387618666 269
0.2%
0.387363666 377
0.3%
0.3861606495 272
0.2%

STInt
Real number (ℝ)

Distinct380
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0010968872
Minimum-0.012214004
Maximum0.024865262
Zeros0
Zeros (%)0.0%
Negative76147
Negative (%)54.8%
Memory size1.1 MiB
2024-03-01T13:05:24.975254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.012214004
5-th percentile-0.011607765
Q1-0.011071139
median-0.003467288
Q30.008124856
95-th percentile0.013070641
Maximum0.024865262
Range0.037079266
Interquartile range (IQR)0.019195995

Descriptive statistics

Standard deviation0.0094964401
Coefficient of variation (CV)-8.6576266
Kurtosis-1.0235067
Mean-0.0010968872
Median Absolute Deviation (MAD)0.007958286
Skewness0.42761455
Sum-152.29731
Variance9.0182374 × 10-5
MonotonicityNot monotonic
2024-03-01T13:05:25.026632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.011587477 2818
 
2.0%
-0.011668229 2015
 
1.5%
-0.011527012 1842
 
1.3%
-0.0115473 1474
 
1.1%
-0.011486835 1442
 
1.0%
-0.011506725 1322
 
1.0%
-0.011647942 1221
 
0.9%
-0.011385795 1177
 
0.8%
-0.011627654 1116
 
0.8%
-0.011304247 1030
 
0.7%
Other values (370) 123388
88.9%
ValueCountFrequency (%)
-0.012214004 306
 
0.2%
-0.011748982 415
 
0.3%
-0.011728694 371
 
0.3%
-0.011668229 2015
1.5%
-0.011658284 536
 
0.4%
-0.011647942 1221
0.9%
-0.011628052 820
 
0.6%
-0.011627654 1116
 
0.8%
-0.011607765 680
 
0.5%
-0.011587477 2818
2.0%
ValueCountFrequency (%)
0.024865262 180
0.1%
0.024025915 179
0.1%
0.023767348 179
0.1%
0.023226347 180
0.1%
0.022760927 177
0.1%
0.021579476 176
0.1%
0.02108621 179
0.1%
0.020835599 181
0.1%
0.020771952 182
0.1%
0.020398025 174
0.1%

dtdlevel
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115141
Distinct (%)92.6%
Missing14551
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean4.2991202
Minimum-1.1168851
Maximum118.31697
Zeros0
Zeros (%)0.0%
Negative1900
Negative (%)1.4%
Memory size1.1 MiB
2024-03-01T13:05:25.077001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1168851
5-th percentile0.53772582
Q11.9760642
median3.3710501
Q35.1322301
95-th percentile9.9480426
Maximum118.31697
Range119.43385
Interquartile range (IQR)3.1561659

Descriptive statistics

Standard deviation5.5270212
Coefficient of variation (CV)1.2856168
Kurtosis168.64347
Mean4.2991202
Median Absolute Deviation (MAD)1.5344607
Skewness10.366191
Sum534354.85
Variance30.547963
MonotonicityNot monotonic
2024-03-01T13:05:25.127043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118.3169675 53
 
< 0.1%
-1.116885091 17
 
< 0.1%
5.73521942 4
 
< 0.1%
5.702660791 4
 
< 0.1%
5.643814216 4
 
< 0.1%
5.603357441 4
 
< 0.1%
5.602988493 4
 
< 0.1%
5.687682295 4
 
< 0.1%
5.793059361 4
 
< 0.1%
4.989592467 4
 
< 0.1%
Other values (115131) 124192
89.4%
(Missing) 14551
 
10.5%
ValueCountFrequency (%)
-1.116885091 17
< 0.1%
-1.111951219 1
 
< 0.1%
-1.106957554 1
 
< 0.1%
-1.091353925 1
 
< 0.1%
-1.063364547 1
 
< 0.1%
-1.056525888 1
 
< 0.1%
-1.054957738 1
 
< 0.1%
-1.040832177 1
 
< 0.1%
-1.039147793 1
 
< 0.1%
-1.038945068 2
 
< 0.1%
ValueCountFrequency (%)
118.3169675 53
< 0.1%
117.0148285 1
 
< 0.1%
116.1978617 1
 
< 0.1%
114.5963057 1
 
< 0.1%
114.2586449 1
 
< 0.1%
114.1202515 1
 
< 0.1%
113.3098507 1
 
< 0.1%
113.1156432 1
 
< 0.1%
112.8588047 1
 
< 0.1%
111.5316221 1
 
< 0.1%

dtdtrend
Real number (ℝ)

MISSING 

Distinct113877
Distinct (%)91.6%
Missing14551
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean-0.16537307
Minimum-47.167559
Maximum15.157938
Zeros1350
Zeros (%)1.0%
Negative63866
Negative (%)46.0%
Memory size1.1 MiB
2024-03-01T13:05:25.174937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-47.167559
5-th percentile-2.0881589
Q1-0.62826789
median-0.025982741
Q30.50830475
95-th percentile1.6370484
Maximum15.157938
Range62.325497
Interquartile range (IQR)1.1365726

Descriptive statistics

Standard deviation2.1642078
Coefficient of variation (CV)-13.086821
Kurtosis244.98052
Mean-0.16537307
Median Absolute Deviation (MAD)0.56491424
Skewness-11.953834
Sum-20554.881
Variance4.6837954
MonotonicityNot monotonic
2024-03-01T13:05:25.223848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1350
 
1.0%
-47.16755948 110
 
0.1%
15.15793786 36
 
< 0.1%
-1.040891429 4
 
< 0.1%
-0.463413691 4
 
< 0.1%
-0.389136497 4
 
< 0.1%
-0.139299649 4
 
< 0.1%
0.1186395204 4
 
< 0.1%
-0.02125906 4
 
< 0.1%
-0.150985797 4
 
< 0.1%
Other values (113867) 122770
88.4%
(Missing) 14551
 
10.5%
ValueCountFrequency (%)
-47.16755948 110
0.1%
-46.73164008 1
 
< 0.1%
-46.60106361 1
 
< 0.1%
-46.4957344 1
 
< 0.1%
-46.21911642 1
 
< 0.1%
-45.81706133 1
 
< 0.1%
-45.81101048 1
 
< 0.1%
-45.26144951 1
 
< 0.1%
-45.18433658 1
 
< 0.1%
-44.45940874 1
 
< 0.1%
ValueCountFrequency (%)
15.15793786 36
< 0.1%
15.08426836 1
 
< 0.1%
14.85836332 1
 
< 0.1%
14.83878119 1
 
< 0.1%
14.67965219 1
 
< 0.1%
14.59354619 1
 
< 0.1%
14.38091524 1
 
< 0.1%
14.15669712 1
 
< 0.1%
14.14431982 1
 
< 0.1%
14.05498174 1
 
< 0.1%

liqnonfinlevel
Real number (ℝ)

MISSING 

Distinct109635
Distinct (%)89.9%
Missing16952
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.44777511
Minimum-4.2180651
Maximum4.789012
Zeros203
Zeros (%)0.1%
Negative34886
Negative (%)25.1%
Memory size1.1 MiB
2024-03-01T13:05:25.273691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-4.2180651
5-th percentile-0.71946541
Q1-0.055897606
median0.34282907
Q30.89180212
95-th percentile1.9453205
Maximum4.789012
Range9.0070771
Interquartile range (IQR)0.94769973

Descriptive statistics

Standard deviation0.85755859
Coefficient of variation (CV)1.9151547
Kurtosis2.5515264
Mean0.44777511
Median Absolute Deviation (MAD)0.45906846
Skewness0.40433252
Sum54580.651
Variance0.73540674
MonotonicityNot monotonic
2024-03-01T13:05:25.323082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203
 
0.1%
-4.218065107 45
 
< 0.1%
0.7526221567 38
 
< 0.1%
0.9036635029 26
 
< 0.1%
-0.017452071 24
 
< 0.1%
0.9036635029 16
 
< 0.1%
-0.068431704 16
 
< 0.1%
4.789012003 15
 
< 0.1%
1.811759502 14
 
< 0.1%
2.079441542 13
 
< 0.1%
Other values (109625) 121483
87.5%
(Missing) 16952
 
12.2%
ValueCountFrequency (%)
-4.218065107 45
< 0.1%
-4.2064226 1
 
< 0.1%
-4.204077352 2
 
< 0.1%
-4.19178299 1
 
< 0.1%
-4.187398912 1
 
< 0.1%
-4.183014834 1
 
< 0.1%
-4.178630756 1
 
< 0.1%
-4.169744989 1
 
< 0.1%
-4.160859222 1
 
< 0.1%
-4.109173176 1
 
< 0.1%
ValueCountFrequency (%)
4.789012003 15
< 0.1%
4.761431703 1
 
< 0.1%
4.752435019 1
 
< 0.1%
4.676610765 1
 
< 0.1%
4.674360969 1
 
< 0.1%
4.631897193 1
 
< 0.1%
4.626357863 1
 
< 0.1%
4.598922696 1
 
< 0.1%
4.590481041 1
 
< 0.1%
4.570124925 1
 
< 0.1%

liqnonfintrend
Real number (ℝ)

MISSING  ZEROS 

Distinct108244
Distinct (%)88.8%
Missing16952
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean-0.020477582
Minimum-2.5826213
Maximum2.7124531
Zeros4151
Zeros (%)3.0%
Negative61960
Negative (%)44.6%
Memory size1.1 MiB
2024-03-01T13:05:25.370928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5826213
5-th percentile-0.58289404
Q1-0.14353002
median-0.003240231
Q30.10750368
95-th percentile0.52033792
Maximum2.7124531
Range5.2950745
Interquartile range (IQR)0.2510337

Descriptive statistics

Standard deviation0.38586944
Coefficient of variation (CV)-18.843506
Kurtosis10.206208
Mean-0.020477582
Median Absolute Deviation (MAD)0.12505585
Skewness-0.16278761
Sum-2496.0739
Variance0.14889522
MonotonicityNot monotonic
2024-03-01T13:05:25.419475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4151
 
3.0%
-2.582621346 123
 
0.1%
2.712453109 76
 
0.1%
1.110223025 × 10-1655
 
< 0.1%
5.551115123 × 10-1752
 
< 0.1%
2.220446049 × 10-1649
 
< 0.1%
-4.44 × 10-1639
 
< 0.1%
-2.22 × 10-1633
 
< 0.1%
2.775557562 × 10-1730
 
< 0.1%
4.440892099 × 10-1626
 
< 0.1%
Other values (108234) 117259
84.5%
(Missing) 16952
 
12.2%
ValueCountFrequency (%)
-2.582621346 123
0.1%
-2.564436361 1
 
< 0.1%
-2.563153566 1
 
< 0.1%
-2.562565942 1
 
< 0.1%
-2.562531044 1
 
< 0.1%
-2.555993515 1
 
< 0.1%
-2.553724234 1
 
< 0.1%
-2.551007778 1
 
< 0.1%
-2.550851039 1
 
< 0.1%
-2.54547765 1
 
< 0.1%
ValueCountFrequency (%)
2.712453109 76
0.1%
2.708526465 1
 
< 0.1%
2.702710696 1
 
< 0.1%
2.676394306 1
 
< 0.1%
2.667518179 1
 
< 0.1%
2.661802009 1
 
< 0.1%
2.642311925 1
 
< 0.1%
2.640658695 1
 
< 0.1%
2.640166374 1
 
< 0.1%
2.611475736 1
 
< 0.1%

ni2talevel
Real number (ℝ)

MISSING 

Distinct115606
Distinct (%)89.0%
Missing8953
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean0.0016797939
Minimum-1.4283198
Maximum0.22986495
Zeros29
Zeros (%)< 0.1%
Negative47970
Negative (%)34.5%
Memory size1.1 MiB
2024-03-01T13:05:25.564618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.4283198
5-th percentile-0.034071917
Q1-0.003787868
median0.002115606
Q30.0061753045
95-th percentile0.020305281
Maximum0.22986495
Range1.6581848
Interquartile range (IQR)0.0099631725

Descriptive statistics

Standard deviation0.03900756
Coefficient of variation (CV)23.221634
Kurtosis128.304
Mean0.0016797939
Median Absolute Deviation (MAD)0.004700537
Skewness-2.0229448
Sum218.19179
Variance0.0015215897
MonotonicityNot monotonic
2024-03-01T13:05:25.614982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2298649538 1612
 
1.2%
-0.018652636 48
 
< 0.1%
0.006652127 48
 
< 0.1%
0 29
 
< 0.1%
-0.005703345 24
 
< 0.1%
0.007225708 18
 
< 0.1%
0.001422808 16
 
< 0.1%
-0.158508159 14
 
< 0.1%
-0.023298238 14
 
< 0.1%
0.059582591 13
 
< 0.1%
Other values (115596) 128056
92.2%
(Missing) 8953
 
6.4%
ValueCountFrequency (%)
-1.428319825 2
< 0.1%
-1.175699989 1
 
< 0.1%
-1.137743958 4
< 0.1%
-1.122115429 1
 
< 0.1%
-1.117161473 1
 
< 0.1%
-1.078875945 1
 
< 0.1%
-1.078151108 1
 
< 0.1%
-1.076482938 1
 
< 0.1%
-1.035642257 1
 
< 0.1%
-0.982051901 1
 
< 0.1%
ValueCountFrequency (%)
0.2298649538 1612
1.2%
0.2298623603 1
 
< 0.1%
0.229709554 1
 
< 0.1%
0.2292476561 1
 
< 0.1%
0.2292412976 1
 
< 0.1%
0.2291022061 1
 
< 0.1%
0.229090997 1
 
< 0.1%
0.2288298434 1
 
< 0.1%
0.228098656 1
 
< 0.1%
0.2273837558 1
 
< 0.1%

ni2tatrend
Real number (ℝ)

MISSING  ZEROS 

Distinct115204
Distinct (%)88.7%
Missing8953
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean-0.00016104928
Minimum-0.64578297
Maximum0.67923669
Zeros4405
Zeros (%)3.2%
Negative59997
Negative (%)43.2%
Memory size1.1 MiB
2024-03-01T13:05:25.664983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.64578297
5-th percentile-0.020404064
Q1-0.002495671
median2.2055927 × 10-5
Q30.0030656153
95-th percentile0.020342929
Maximum0.67923669
Range1.3250197
Interquartile range (IQR)0.0055612863

Descriptive statistics

Standard deviation0.035835985
Coefficient of variation (CV)-222.51565
Kurtosis183.23216
Mean-0.00016104928
Median Absolute Deviation (MAD)0.0027771975
Skewness0.47449378
Sum-20.919013
Variance0.0012842178
MonotonicityNot monotonic
2024-03-01T13:05:25.716346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4405
 
3.2%
0.6792366888 82
 
0.1%
-0.645782973 77
 
0.1%
3.469446952 × 10-1846
 
< 0.1%
-2.78 × 10-1727
 
< 0.1%
-8.67 × 10-1919
 
< 0.1%
1.734723476 × 10-1816
 
< 0.1%
8.67361738 × 10-1916
 
< 0.1%
5.551115123 × 10-1715
 
< 0.1%
-2.08 × 10-1713
 
< 0.1%
Other values (115194) 125176
90.2%
(Missing) 8953
 
6.4%
ValueCountFrequency (%)
-0.645782973 77
0.1%
-0.640884751 1
 
< 0.1%
-0.615084129 1
 
< 0.1%
-0.610258337 1
 
< 0.1%
-0.59932527 1
 
< 0.1%
-0.587791049 1
 
< 0.1%
-0.580944265 1
 
< 0.1%
-0.570222007 1
 
< 0.1%
-0.569720936 1
 
< 0.1%
-0.535224521 1
 
< 0.1%
ValueCountFrequency (%)
0.6792366888 82
0.1%
0.6739098819 1
 
< 0.1%
0.6688168704 1
 
< 0.1%
0.660817587 1
 
< 0.1%
0.6567731211 1
 
< 0.1%
0.6510265873 1
 
< 0.1%
0.6430373176 1
 
< 0.1%
0.6350133356 1
 
< 0.1%
0.6048266666 1
 
< 0.1%
0.5954457366 1
 
< 0.1%

sizelevel
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct126695
Distinct (%)92.5%
Missing1949
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean0.42455869
Minimum-6.1887801
Maximum6.7782683
Zeros0
Zeros (%)0.0%
Negative57845
Negative (%)41.7%
Memory size1.1 MiB
2024-03-01T13:05:25.766560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-6.1887801
5-th percentile-2.8899942
Q1-0.99561439
median0.41171961
Q31.7808255
95-th percentile3.8676796
Maximum6.7782683
Range12.967048
Interquartile range (IQR)2.7764399

Descriptive statistics

Standard deviation2.0595991
Coefficient of variation (CV)4.8511529
Kurtosis-0.13408014
Mean0.42455869
Median Absolute Deviation (MAD)1.3882063
Skewness0.13347828
Sum58120.386
Variance4.2419485
MonotonicityNot monotonic
2024-03-01T13:05:25.820129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.778268339 252
 
0.2%
-6.188780098 12
 
< 0.1%
2.834357012 4
 
< 0.1%
2.885069259 4
 
< 0.1%
2.863618041 4
 
< 0.1%
3.909812858 4
 
< 0.1%
2.798032233 4
 
< 0.1%
2.77365014 4
 
< 0.1%
2.73498344 4
 
< 0.1%
2.706705073 4
 
< 0.1%
Other values (126685) 136600
98.4%
(Missing) 1949
 
1.4%
ValueCountFrequency (%)
-6.188780098 12
< 0.1%
-6.089806726 1
 
< 0.1%
-5.875153245 3
 
< 0.1%
-5.744644401 1
 
< 0.1%
-5.698877343 1
 
< 0.1%
-5.697605796 1
 
< 0.1%
-5.688910003 1
 
< 0.1%
-5.677978683 1
 
< 0.1%
-5.67274501 3
 
< 0.1%
-5.666401065 1
 
< 0.1%
ValueCountFrequency (%)
6.778268339 252
0.2%
6.777981909 1
 
< 0.1%
6.777030109 1
 
< 0.1%
6.775192578 1
 
< 0.1%
6.774006875 1
 
< 0.1%
6.771083525 1
 
< 0.1%
6.768626457 4
 
< 0.1%
6.766020834 1
 
< 0.1%
6.759925656 4
 
< 0.1%
6.756083168 1
 
< 0.1%

sizetrend
Real number (ℝ)

MISSING  ZEROS 

Distinct125361
Distinct (%)91.6%
Missing1949
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean-0.010516125
Minimum-1.9274249
Maximum2.0238423
Zeros1451
Zeros (%)1.0%
Negative67844
Negative (%)48.9%
Memory size1.1 MiB
2024-03-01T13:05:25.871352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9274249
5-th percentile-0.61174016
Q1-0.1699208
median0
Q30.16605711
95-th percentile0.52716534
Maximum2.0238423
Range3.9512672
Interquartile range (IQR)0.3359779

Descriptive statistics

Standard deviation0.37084877
Coefficient of variation (CV)-35.264774
Kurtosis4.9625575
Mean-0.010516125
Median Absolute Deviation (MAD)0.16804698
Skewness-0.1375372
Sum-1439.6154
Variance0.13752881
MonotonicityNot monotonic
2024-03-01T13:05:25.922981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1451
 
1.0%
2.023842297 195
 
0.1%
-1.927424889 163
 
0.1%
0.048737225 11
 
< 0.1%
0.020186838 6
 
< 0.1%
-0.010317172 6
 
< 0.1%
0.066758168 5
 
< 0.1%
0.00089401 4
 
< 0.1%
-0.108788864 4
 
< 0.1%
-0.065167374 4
 
< 0.1%
Other values (125351) 135047
97.3%
(Missing) 1949
 
1.4%
ValueCountFrequency (%)
-1.927424889 163
0.1%
-1.925930284 1
 
< 0.1%
-1.920901263 1
 
< 0.1%
-1.915651948 1
 
< 0.1%
-1.91163673 1
 
< 0.1%
-1.910493458 1
 
< 0.1%
-1.90355336 1
 
< 0.1%
-1.903420821 1
 
< 0.1%
-1.898056977 1
 
< 0.1%
-1.893912536 3
 
< 0.1%
ValueCountFrequency (%)
2.023842297 195
0.1%
2.019286397 1
 
< 0.1%
2.014178854 1
 
< 0.1%
2.009280808 1
 
< 0.1%
2.007782525 4
 
< 0.1%
1.997518285 1
 
< 0.1%
1.9962127 1
 
< 0.1%
1.995992341 1
 
< 0.1%
1.995804224 1
 
< 0.1%
1.992978812 1
 
< 0.1%

m2b
Real number (ℝ)

MISSING 

Distinct119942
Distinct (%)92.5%
Missing9145
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean2.2674856
Minimum0.15081204
Maximum104.05071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-01T13:05:25.975828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.15081204
5-th percentile0.51161501
Q10.77610707
median1.0186965
Q31.4475467
95-th percentile4.2519716
Maximum104.05071
Range103.8999
Interquartile range (IQR)0.6714396

Descriptive statistics

Standard deviation7.9128966
Coefficient of variation (CV)3.489723
Kurtosis108.99113
Mean2.2674856
Median Absolute Deviation (MAD)0.29501344
Skewness9.8849769
Sum294092.88
Variance62.613932
MonotonicityNot monotonic
2024-03-01T13:05:26.026852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.0507124 372
 
0.3%
0.1508120391 56
 
< 0.1%
1 16
 
< 0.1%
0.6051362031 4
 
< 0.1%
1.816632591 4
 
< 0.1%
0.9716759411 4
 
< 0.1%
0.8967788394 4
 
< 0.1%
0.928001477 4
 
< 0.1%
0.9984934569 4
 
< 0.1%
0.910231488 4
 
< 0.1%
Other values (119932) 129228
93.1%
(Missing) 9145
 
6.6%
ValueCountFrequency (%)
0.1508120391 56
< 0.1%
0.1513299782 1
 
< 0.1%
0.152787593 1
 
< 0.1%
0.1530961874 1
 
< 0.1%
0.1540299815 1
 
< 0.1%
0.157033924 1
 
< 0.1%
0.1573883875 1
 
< 0.1%
0.15802543 1
 
< 0.1%
0.1582560461 1
 
< 0.1%
0.1595983522 1
 
< 0.1%
ValueCountFrequency (%)
104.0507124 372
0.3%
103.9444664 1
 
< 0.1%
103.8373914 1
 
< 0.1%
103.7406795 1
 
< 0.1%
103.4360089 1
 
< 0.1%
103.4158101 1
 
< 0.1%
103.0042729 1
 
< 0.1%
102.6811976 1
 
< 0.1%
102.4328174 1
 
< 0.1%
102.2519125 1
 
< 0.1%

sigma
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct122216
Distinct (%)92.7%
Missing6969
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.17636983
Minimum0.009583336
Maximum1.1402662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-01T13:05:26.076387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.009583336
5-th percentile0.058280323
Q10.097473197
median0.14604218
Q30.2190869
95-th percentile0.39489251
Maximum1.1402662
Range1.1306829
Interquartile range (IQR)0.12161371

Descriptive statistics

Standard deviation0.11676618
Coefficient of variation (CV)0.66205301
Kurtosis9.2186521
Mean0.17636983
Median Absolute Deviation (MAD)0.056266434
Skewness2.2951434
Sum23258.948
Variance0.013634341
MonotonicityNot monotonic
2024-03-01T13:05:26.126662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.140266224 91
 
0.1%
0.009583336 38
 
< 0.1%
0.1661395308 18
 
< 0.1%
0.1599151669 12
 
< 0.1%
0.106509273 4
 
< 0.1%
0.1504973261 4
 
< 0.1%
0.146868491 4
 
< 0.1%
0.1202854074 4
 
< 0.1%
0.1142835172 4
 
< 0.1%
0.1094187959 4
 
< 0.1%
Other values (122206) 131693
94.8%
(Missing) 6969
 
5.0%
ValueCountFrequency (%)
0.009583336 38
< 0.1%
0.009584397 1
 
< 0.1%
0.009659634 1
 
< 0.1%
0.009858566 1
 
< 0.1%
0.009945627 1
 
< 0.1%
0.010167408 1
 
< 0.1%
0.010378297 1
 
< 0.1%
0.010406415 1
 
< 0.1%
0.010412391 1
 
< 0.1%
0.010536172 1
 
< 0.1%
ValueCountFrequency (%)
1.140266224 91
0.1%
1.136656805 1
 
< 0.1%
1.129626418 1
 
< 0.1%
1.124353092 1
 
< 0.1%
1.120859335 1
 
< 0.1%
1.120319544 1
 
< 0.1%
1.111296276 1
 
< 0.1%
1.111035583 1
 
< 0.1%
1.108661714 1
 
< 0.1%
1.105889438 1
 
< 0.1%

DTDmedianNonFin
Real number (ℝ)

MISSING 

Distinct396
Distinct (%)0.3%
Missing6500
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean3.6086275
Minimum1.6358092
Maximum5.4149882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-01T13:05:26.175742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.6358092
5-th percentile2.1953595
Q13.0051335
median3.5004305
Q34.434691
95-th percentile5.0768855
Maximum5.4149882
Range3.7791791
Interquartile range (IQR)1.4295575

Descriptive statistics

Standard deviation0.90571893
Coefficient of variation (CV)0.25098709
Kurtosis-0.92307786
Mean3.6086275
Median Absolute Deviation (MAD)0.69614967
Skewness0.069523149
Sum477583.81
Variance0.82032679
MonotonicityNot monotonic
2024-03-01T13:05:26.226996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.423880075 419
 
0.3%
5.04642172 415
 
0.3%
4.234486286 415
 
0.3%
5.022839889 415
 
0.3%
5.235503283 415
 
0.3%
5.197767451 414
 
0.3%
5.359349579 412
 
0.3%
4.835045231 412
 
0.3%
4.895286094 412
 
0.3%
4.854561228 411
 
0.3%
Other values (386) 128205
92.3%
(Missing) 6500
 
4.7%
ValueCountFrequency (%)
1.635809169 381
0.3%
1.734807906 377
0.3%
1.791268535 382
0.3%
1.849498135 383
0.3%
1.860648049 384
0.3%
1.863507497 324
0.2%
1.885356779 327
0.2%
1.918101868 319
0.2%
1.935938953 377
0.3%
1.935962673 323
0.2%
ValueCountFrequency (%)
5.41498823 400
0.3%
5.387167933 402
0.3%
5.359349579 412
0.3%
5.351843201 398
0.3%
5.340295454 399
0.3%
5.307231783 403
0.3%
5.305463992 400
0.3%
5.293061995 395
0.3%
5.269803654 402
0.3%
5.252275721 392
0.3%
Distinct973
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-03-01T13:05:26.360387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length51
Median length35
Mean length20.190032
Min length5

Characters and Unicode

Total characters2803285
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFutureFuel Corp
2nd rowFutureFuel Corp
3rd rowFutureFuel Corp
4th rowFutureFuel Corp
5th rowFutureFuel Corp
ValueCountFrequency (%)
inc 55405
 
12.5%
corp 36090
 
8.1%
energy 32675
 
7.4%
lp 13247
 
3.0%
resources 13168
 
3.0%
co 12352
 
2.8%
partners 9358
 
2.1%
oil 7796
 
1.8%
petroleum 6093
 
1.4%
trust 5831
 
1.3%
Other values (1015) 251422
56.7%
2024-03-01T13:05:26.565874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
304592
 
10.9%
r 231740
 
8.3%
e 230018
 
8.2%
n 215347
 
7.7%
o 187749
 
6.7%
a 132079
 
4.7%
s 119857
 
4.3%
c 118022
 
4.2%
i 111895
 
4.0%
t 105722
 
3.8%
Other values (51) 1046264
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1957846
69.8%
Uppercase Letter 526572
 
18.8%
Space Separator 304592
 
10.9%
Other Punctuation 10676
 
0.4%
Dash Punctuation 2586
 
0.1%
Decimal Number 1013
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 231740
11.8%
e 230018
11.7%
n 215347
11.0%
o 187749
9.6%
a 132079
 
6.7%
s 119857
 
6.1%
c 118022
 
6.0%
i 111895
 
5.7%
t 105722
 
5.4%
l 101550
 
5.2%
Other values (16) 403867
20.6%
Uppercase Letter
ValueCountFrequency (%)
C 77632
14.7%
I 71147
13.5%
E 53567
10.2%
P 53239
10.1%
L 32374
 
6.1%
S 28519
 
5.4%
R 27416
 
5.2%
T 23663
 
4.5%
O 22500
 
4.3%
G 20445
 
3.9%
Other values (16) 116070
22.0%
Other Punctuation
ValueCountFrequency (%)
/ 5364
50.2%
& 5165
48.4%
. 108
 
1.0%
' 39
 
0.4%
Decimal Number
ValueCountFrequency (%)
6 490
48.4%
3 378
37.3%
1 145
 
14.3%
Space Separator
ValueCountFrequency (%)
304592
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2484418
88.6%
Common 318867
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 231740
 
9.3%
e 230018
 
9.3%
n 215347
 
8.7%
o 187749
 
7.6%
a 132079
 
5.3%
s 119857
 
4.8%
c 118022
 
4.8%
i 111895
 
4.5%
t 105722
 
4.3%
l 101550
 
4.1%
Other values (42) 930439
37.5%
Common
ValueCountFrequency (%)
304592
95.5%
/ 5364
 
1.7%
& 5165
 
1.6%
- 2586
 
0.8%
6 490
 
0.2%
3 378
 
0.1%
1 145
 
< 0.1%
. 108
 
< 0.1%
' 39
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2803285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
304592
 
10.9%
r 231740
 
8.3%
e 230018
 
8.2%
n 215347
 
7.7%
o 187749
 
6.7%
a 132079
 
4.7%
s 119857
 
4.3%
c 118022
 
4.2%
i 111895
 
4.0%
t 105722
 
3.8%
Other values (51) 1046264
37.3%

INDUSTRY2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing34
Missing (%)< 0.1%
Memory size1.1 MiB
131010.0
89502 
131011.0
34476 
131110.0
14833 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1110488
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row131110.0
2nd row131110.0
3rd row131110.0
4th row131110.0
5th row131110.0

Common Values

ValueCountFrequency (%)
131010.0 89502
64.5%
131011.0 34476
 
24.8%
131110.0 14833
 
10.7%
(Missing) 34
 
< 0.1%

Length

2024-03-01T13:05:26.636671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T13:05:26.674795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
131010.0 89502
64.5%
131011.0 34476
 
24.8%
131110.0 14833
 
10.7%

Most occurring characters

ValueCountFrequency (%)
1 465742
41.9%
0 367124
33.1%
3 138811
 
12.5%
. 138811
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 971677
87.5%
Other Punctuation 138811
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 465742
47.9%
0 367124
37.8%
3 138811
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 138811
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1110488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 465742
41.9%
0 367124
33.1%
3 138811
 
12.5%
. 138811
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1110488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 465742
41.9%
0 367124
33.1%
3 138811
 
12.5%
. 138811
 
12.5%

Date
Date

Distinct432
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum1988-01-31 00:00:00
Maximum2023-12-31 00:00:00
2024-03-01T13:05:26.721163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:26.773414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Y
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
136031 
1
 
2814

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters138845
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136031
98.0%
1 2814
 
2.0%

Length

2024-03-01T13:05:26.819517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-01T13:05:26.853893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 136031
98.0%
1 2814
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 136031
98.0%
1 2814
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 138845
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136031
98.0%
1 2814
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 138845
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136031
98.0%
1 2814
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136031
98.0%
1 2814
 
2.0%

Interactions

2024-03-01T13:05:23.542119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:15.827423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.562741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.113833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.693679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.331500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.887730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.423953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.961809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.536026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.191199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.752894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.352774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.895693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.586280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:15.897932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.604326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.157789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.734406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.373070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.927944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.464610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.006514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.577166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.233308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.797754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.392464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.937382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.625601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:15.976500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.640166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.195410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.772782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.410118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.963059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.500243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.044379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.614532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.271432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.837538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.429269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.974720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.667490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.074277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.679673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.238573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.811211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.449431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.002749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.539319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.087722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.655749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.313043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.881335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.469949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.015166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.708572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.131849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.718676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.277976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.849902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.488696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.040823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.577315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.127831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.790885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.353387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.923161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.507887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.055133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.750596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.177466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.757948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.316916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.888974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.528624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.078593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.615581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.168672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.829910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.392243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.965116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.545620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.093454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.789342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.217230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.798632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.356920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.924835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.565496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.115281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.651848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.206479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.868586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.429279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.003914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.580716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.129067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.828153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.260958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.835128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.394779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.963024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.602847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.150309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.687194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.243883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.905751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.466341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.042685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.617467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.166764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.872479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.305235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.875352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.438170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.003027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.644206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.191069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.727054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.286416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.947817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.506846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.087534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.657424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.206792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.915651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.349534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.914850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.479646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.131276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.685279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.229269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.765404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.327383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.986970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.548128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.130426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.697147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.247375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.957505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.392065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.953824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.527413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.169999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.723518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.266797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.805806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.368672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.027541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.587026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.173530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.736076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.286142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:24.003128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.436461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.996719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.571217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.212155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.766721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.308553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.847164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.412440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.069484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.630724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.217792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.778488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.327589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:24.042492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.476497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.033656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.610714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.249946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.804568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.344828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.883028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.451784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.108588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.669062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.259190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.814895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.462316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:24.083241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:16.517491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.072168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:17.650347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.288366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:18.844770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.382460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:19.920883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:20.490652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.146398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:21.708445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.306530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:22.851318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-01T13:05:23.498759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-01T13:05:26.884203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CompNoDTDmedianNonFinINDUSTRY2STIntStkIndxYdtdleveldtdtrendliqnonfinlevelliqnonfintrendm2bni2talevelni2tatrendsigmasizelevelsizetrend
CompNo1.0000.1610.192-0.244-0.0290.050-0.168-0.0470.003-0.027-0.115-0.154-0.0020.204-0.199-0.047
DTDmedianNonFin0.1611.0000.029-0.4060.3170.0880.2100.096-0.017-0.006-0.0570.0210.007-0.3020.027-0.055
INDUSTRY20.1920.0291.000-0.025-0.0110.018-0.1260.0030.4240.005-0.051-0.121-0.0090.185-0.108-0.034
STInt-0.244-0.406-0.0251.0000.0700.1080.0180.0180.052-0.0100.1460.0810.0190.056-0.0240.151
StkIndx-0.0290.317-0.0110.0701.0000.0550.0200.226-0.004-0.009-0.040-0.0540.024-0.080-0.0120.044
Y0.0500.0880.0180.1080.0551.000-0.175-0.100-0.067-0.033-0.127-0.143-0.0310.148-0.109-0.155
dtdlevel-0.1680.210-0.1260.0180.020-0.1751.000-0.0690.2100.0060.4190.499-0.012-0.7300.4810.050
dtdtrend-0.0470.0960.0030.0180.226-0.100-0.0691.0000.0130.1250.1140.0230.087-0.1590.0420.374
liqnonfinlevel0.003-0.0170.4240.052-0.004-0.0670.2100.0131.000-0.0570.1170.059-0.0270.031-0.110-0.008
liqnonfintrend-0.027-0.0060.005-0.010-0.009-0.0330.0060.125-0.0571.0000.0100.1020.068-0.0110.0470.066
m2b-0.115-0.057-0.0510.146-0.040-0.1270.4190.1140.1170.0101.0000.2960.071-0.1420.2020.330
ni2talevel-0.1540.021-0.1210.081-0.054-0.1430.4990.0230.0590.1020.2961.000-0.147-0.4470.3280.104
ni2tatrend-0.0020.007-0.0090.0190.024-0.031-0.0120.087-0.0270.0680.071-0.1471.0000.013-0.0040.146
sigma0.204-0.3020.1850.056-0.0800.148-0.730-0.1590.031-0.011-0.142-0.4470.0131.000-0.633-0.031
sizelevel-0.1990.027-0.108-0.024-0.012-0.1090.4810.042-0.1100.0470.2020.328-0.004-0.6331.0000.068
sizetrend-0.047-0.055-0.0340.1510.044-0.1550.0500.374-0.0080.0660.3300.1040.146-0.0310.0681.000

Missing values

2024-03-01T13:05:24.150129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-01T13:05:24.323188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CompNoStkIndxSTIntdtdleveldtdtrendliqnonfinlevelliqnonfintrendni2talevelni2tatrendsizelevelsizetrendm2bsigmaDTDmedianNonFinCompany_nameINDUSTRY2DateY
0270140.133740-0.011305NaNNaNNaNNaNNaNNaN0.0985220.000000NaNNaN4.439600FutureFuel Corp131110.03/31/20110
1270140.129957-0.011527NaNNaNNaNNaNNaNNaN0.1716940.073172NaNNaN4.525509FutureFuel Corp131110.04/30/20110
2270140.232883-0.011487NaNNaN1.7315340.0000000.0026570.0000000.2117180.0800461.351018NaN4.615192FutureFuel Corp131110.05/31/20110
3270140.281292-0.0116289.8388300.0000001.7315340.0000000.0026570.0000000.194665-0.0511591.2382260.0882304.581457FutureFuel Corp131110.06/30/20110
4270140.173168-0.01130410.1601070.3212781.7315340.0000000.0026570.0000000.182296-0.0494751.2860890.0819304.557544FutureFuel Corp131110.07/31/20110
5270140.161589-0.0116288.820029-2.6801571.8121880.2419600.0039430.0038590.162507-0.0989421.2459080.0958253.890736FutureFuel Corp131110.08/31/20110
627014-0.008570-0.0115877.922600-2.6922841.8605790.1935680.0047150.0030870.136981-0.1531591.2518590.1024523.503146FutureFuel Corp131110.09/30/20110
7270140.058870-0.0117297.317280-2.4212821.8928410.1613070.0052290.0025730.127665-0.0652091.2610030.1089313.653213FutureFuel Corp131110.010/31/20110
8270140.056253-0.0116686.791160-2.6306021.9037110.0652200.0061130.0053040.1327520.0406961.3800500.1255573.543607FutureFuel Corp131110.011/30/20110
927014-0.000223-0.0116286.420410-2.2244951.9118630.0570680.0067760.0046410.1354790.0245361.3612920.1245013.495836FutureFuel Corp131110.012/31/20110
CompNoStkIndxSTIntdtdleveldtdtrendliqnonfinlevelliqnonfintrendni2talevelni2tatrendsizelevelsizetrendm2bsigmaDTDmedianNonFinCompany_nameINDUSTRY2DateY
1388352125390.0831150.010061NaNNaNNaNNaNNaNNaN-3.247830-0.326275NaNNaN2.963553Turbo Energy SA131110.010/31/20230
1388362125390.1195290.009763NaNNaNNaNNaNNaNNaN-3.2454130.004834NaNNaN3.119818Turbo Energy SA131110.011/30/20230
1388372125390.2391490.009543NaNNaNNaNNaNNaNNaN-3.342075-0.289986NaN0.3928973.316397Turbo Energy SA131110.012/31/20230
1388382126130.0831150.010061NaNNaNNaNNaNNaNNaN1.3108020.000000NaNNaN2.963553Atlas Energy Solutions Inc131011.010/31/20230
1388392126130.1195290.009763NaNNaNNaNNaNNaNNaN1.279521-0.031282NaNNaN3.119818Atlas Energy Solutions Inc131011.011/30/20230
1388402126130.2391490.009543NaNNaNNaNNaNNaNNaN1.268367-0.022306NaN0.4335803.316397Atlas Energy Solutions Inc131011.012/31/20230
1388412127500.0831150.010061NaNNaNNaNNaNNaNNaN1.2712600.000000NaNNaN2.963553Mach Natural Resources LP131010.010/31/20230
1388422127500.1195290.009763NaNNaNNaNNaNNaNNaN1.252952-0.018307NaNNaN3.119818Mach Natural Resources LP131010.011/30/20230
1388432127500.2391490.009543NaNNaNNaNNaNNaNNaN1.219033-0.067838NaNNaN3.316397Mach Natural Resources LP131010.012/31/20230
1388442130580.2391490.009543NaNNaNNaNNaNNaNNaN-1.1814240.000000NaNNaN3.316397Vast Solar Pty Ltd131110.012/31/20230

Duplicate rows

Most frequently occurring

CompNoStkIndxSTIntdtdleveldtdtrendliqnonfinlevelliqnonfintrendni2talevelni2tatrendsizelevelsizetrendm2bsigmaDTDmedianNonFinCompany_nameINDUSTRY2DateY# duplicates
107530321-0.466057-0.0106883.527319-3.5560400.729963-0.015729-0.002895-0.1022740.752774-1.6675360.7627430.2382041.635809ION Geophysical Corp131011.02/28/200904
107630321-0.396787-0.0108693.101828-2.9243170.722956-0.008722-0.012367-0.0928020.610953-1.0979440.7979070.2860721.734808ION Geophysical Corp131011.03/31/200904
107730321-0.389791-0.0107683.982187-3.8709090.736971-0.3489920.0065770.0004430.925377-1.5602600.5844120.2325511.791269ION Geophysical Corp131011.01/31/200904
107830321-0.388841-0.0115074.790406-4.1169440.801635-0.4136570.0064890.0005321.162340-1.1989310.6977130.1756501.860648ION Geophysical Corp131011.011/30/200804
107930321-0.383806-0.0113664.386953-3.5978990.769303-0.3813240.0065330.0004871.067021-0.9227040.7271300.1874961.849498ION Geophysical Corp131011.012/31/200804
108030321-0.373726-0.0099345.190508-2.6310850.8339680.0486590.0064440.0005551.257842-0.6073681.0026230.1514522.208862ION Geophysical Corp131011.010/31/200804
108130321-0.370081-0.0111722.681524-2.2156490.715949-0.001715-0.021839-0.0833300.499197-0.4638170.8556680.2983271.935939ION Geophysical Corp131011.04/30/200904
108230321-0.342654-0.0111522.263148-1.7070370.698930-0.024197-0.0235030.0073210.396134-0.2109090.8948260.3140392.025871ION Geophysical Corp131011.05/31/200904
108330321-0.286191-0.0022374.6422720.0065121.8216130.026003-0.0053680.0055401.346833-0.1333731.1511760.1636132.049791ION Geophysical Corp131011.09/30/200104
108430321-0.281781-0.0109621.834412-1.2559340.681912-0.007179-0.0251680.0089860.293725-0.0099470.9137420.3205312.064571ION Geophysical Corp131011.06/30/200904